Alterations in protein expression and site-specific N-glycosylation of prostate cancer tissues

Microfibril-associated glycoprotein 4 forms octamers that mediate interactions with elastogenic proteins and cells

Microfibril-associated glycoprotein 4 (MFAP4) is a 36-kDa extracellular matrix glycoprotein with critical roles in organ fibrosis, chronic obstructive pulmonary disease, and cardiovascular disorders, including aortic aneurysms. MFAP4 multimerises and interacts with elastogenic proteins, including fibrillin-1 and tropoelastin, and with cells via integrins. Structural details of MFAP4 and its potential interfaces for these interactions are unknown. Here, we present a cryo-electron microscopy structure of human MFAP4. In the presence of calcium, MFAP4 assembles as an octamer, where two sets of homodimers constitute the top and bottom halves of each octamer. Each homodimer is linked together by an intermolecular disulphide bond. A C34S missense mutation prevents disulphide-bond formation between monomers but does not prevent octamer assembly. The atomic model, built into the 3.55 Å cryo-EM map, suggests that salt-bridge interactions mediate homodimer assembly, while non-polar residues form the interface between octamer halves. In the absence of calcium, an MFAP4 octamer dissociates into two tetramers. Binding studies with fibrillin-1, tropoelastin, LTBP4, and small fibulins show that MFAP4 has multiple surfaces for protein-protein interactions, most of which depend upon MFAP4 octamer assembly. The C34S mutation does not affect these protein interactions or cell interactions. MFAP4 assemblies with fibrillin-1 abrogate MFAP4 interactions with cells.

In-Depth Glycoproteomic Assay of Urinary Prostatic Acid Phosphatase

Prostate-specific antigen (PSA) is a well-known clinical biomarker in prostate cancer (PCa) diagnosis, but a better test is still needed, as the serum-level-based PSA quantification exhibits limited specificity and comes with poor predictive value. Prior to PSA, prostatic acid phosphatase (PAP) was used, but it was replaced by PSA because PSA improved the early detection of PCa. Upon revisiting PAP and its glycosylation specifically, it appears to be a promising new biomarker candidate. Namely, previous studies have indicated that PAP glycoforms differ between PCa and non-PCa individuals. However, an in-depth characterization of PAP glycosylation is still lacking. In this study, we established an in-depth glycoproteomic assay for urinary PAP by characterizing both the micro- and macroheterogeneity of the PAP glycoprofile. For this purpose, PAP samples were analyzed by capillary electrophoresis coupled to mass spectrometry after affinity purification from urine and proteolytic digestion. The developed urinary PAP assay was applied on a pooled DRE (digital rectal examination) urine sample from nine individuals. Three glycosylation sites were characterized, namely N94, N220, and N333, via N-glycopeptide analysis. Taking sialic acid linkage isomers into account, a total of 63, 27, and 4 N-glycan structures were identified, respectively. The presented PAP glycoproteomic assay will enable the determination of potential glycomic biomarkers for the early detection and prognosis of PCa in cohort studies.

Capitalizing glycomic changes for improved biomarker-based cancer diagnostics

Cancer serum biomarkers are valuable or even indispensable for cancer diagnostics and/or monitoring and, currently, many cancer serum markers are routinely used in the clinic. Most of those markers are glycoproteins, carrying cancer-specific glycan structures that can provide extra-information for cancer monitoring. Nonetheless, in the majority of cases, this differential feature is not exploited and the corresponding analytical assays detect only the protein amount, disregarding the analysis of the aberrant glycoform. Two exceptions to this trend are the biomarkers α-fetoprotein (AFP) and cancer antigen 19-9 (CA19-9), which are clinically monitored for their cancer-related glycan changes, and only the AFP assay includes quantification of both the protein amount and the altered glycoform. This narrative review demonstrates, through several examples, the advantages of the combined quantification of protein cancer biomarkers and the respective glycoform analysis, which enable to yield the maximum information and overcome the weaknesses of each individual analysis. This strategy allows to achieve higher sensitivity and specificity in the detection of cancer, enhancing the diagnostic power of biomarker-based cancer detection tests.

An analytical study on the identification of N-linked glycosylation sites using machine learning model

N-linked is the most common type of glycosylation which plays a significant role in identifying various diseases such as type I diabetes and cancer and helps in drug development. Most of the proteins cannot perform their biological and psychological functionalities without undergoing such modification. Therefore, it is essential to identify such sites by computational techniques because of experimental limitations. This study aims to analyze and synthesize the progress to discover N-linked places using machine learning methods. It also explores the performance of currently available tools to predict such sites. Almost seventy research articles published in recognized journals of the N-linked glycosylation field have shortlisted after the rigorous filtering process. The findings of the studies have been reported based on multiple aspects: publication channel, feature set construction method, training algorithm, and performance evaluation. Moreover, a literature survey has developed a taxonomy of N-linked sequence identification. Our study focuses on the performance evaluation criteria, and the importance of N-linked glycosylation motivates us to discover resources that use computational methods instead of the experimental method due to its limitations.

From Omics to Multi-Omics Approaches for In-Depth Analysis of the Molecular Mechanisms of Prostate Cancer

Cancer arises following alterations at different cellular levels, including genetic and epigenetic modifications, transcription and translation dysregulation, as well as metabolic variations. High-throughput omics technologies that allow one to identify and quantify processes involved in these changes are now available and have been instrumental in generating a wealth of steadily increasing data from patient tumors, liquid biopsies, and from tumor models. Extensive investigation and integration of these data have led to new biological insights into the origin and development of multiple cancer types and helped to unravel the molecular networks underlying this complex pathology. The comprehensive and quantitative analysis of a molecule class in a biological sample is named omics and large-scale omics studies addressing different prostate cancer stages have been performed in recent years. Prostate tumors represent the second leading cancer type and a prevalent cause of cancer death in men worldwide. It is a very heterogenous disease so that evaluating inter- and intra-tumor differences will be essential for a precise insight into disease development and plasticity, but also for the development of personalized therapies. There is ample evidence for the key role of the androgen receptor, a steroid hormone-activated transcription factor, in driving early and late stages of the disease, and this led to the development and approval of drugs addressing diverse targets along this pathway. Early genomic and transcriptomic studies have allowed one to determine the genes involved in prostate cancer and regulated by androgen signaling or other tumor-relevant signaling pathways. More recently, they have been supplemented by epigenomic, cistromic, proteomic and metabolomic analyses, thus, increasing our knowledge on the intricate mechanisms involved, the various levels of regulation and their interplay. The comprehensive investigation of these omics approaches and their integration into multi-omics analyses have led to a much deeper understanding of the molecular pathways involved in prostate cancer progression, and in response and resistance to therapies. This brings the hope that novel vulnerabilities will be identified, that existing therapies will be more beneficial by targeting the patient population likely to respond best, and that bespoke treatments with increased efficacy will be available soon.

Proteomic Analysis of Lung Cancer Types—A Pilot Study

Lung cancer is the leading cause of tumor-related mortality, therefore significant effort is directed towards understanding molecular alterations occurring at the origin of the disease to improve current treatment options. The aim of our pilot-scale study was to carry out a detailed proteomic analysis of formalin-fixed paraffin-embedded tissue sections from patients with small cell or non-small cell lung cancer (adenocarcinoma, squamous cell carcinoma, and large cell carcinoma). Tissue surface digestion was performed on relatively small cancerous and tumor-adjacent normal regions and differentially expressed proteins were identified using label-free quantitative mass spectrometry and subsequent statistical analysis. Principal component analysis clearly distinguished cancerous and cancer adjacent normal samples, while the four lung cancer types investigated had distinct molecular profiles and gene set enrichment analysis revealed specific dysregulated biological processes as well. Furthermore, proteins with altered expression unique to a specific lung cancer type were identified and could be the targets of future studies.